#region License Information
/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("NMSE Evaluator with shape-constraints (single-objective)", "Calculates NMSE of a symbolic regression solution and checks constraints. The fitness is a combination of NMSE and constraint violations.")]
  [StorableType("27473973-DD8D-4375-997D-942E2280AE8E")]
  public class NMSESingleObjectiveConstraintsEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
    #region Parameter/Properties

    private const string OptimizeParametersParameterName = "OptimizeParameters";
    private const string ParameterOptimizationIterationsParameterName = "ParameterOptimizationIterations";
    private const string UseSoftConstraintsParameterName = "UseSoftConstraintsEvaluation";
    private const string BoundsEstimatorParameterName = "BoundsEstimator";
    private const string PenaltyFactorParameterName = "PenaltyFactor";


    public IFixedValueParameter<BoolValue> OptimizerParametersParameter =>
      (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersParameterName];

    public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter =>
      (IFixedValueParameter<IntValue>)Parameters[ParameterOptimizationIterationsParameterName];

    public IFixedValueParameter<BoolValue> UseSoftConstraintsParameter =>
      (IFixedValueParameter<BoolValue>)Parameters[UseSoftConstraintsParameterName];

    public IValueParameter<IBoundsEstimator> BoundsEstimatorParameter =>
      (IValueParameter<IBoundsEstimator>)Parameters[BoundsEstimatorParameterName];
    public IFixedValueParameter<DoubleValue> PenaltyFactorParameter =>
      (IFixedValueParameter<DoubleValue>)Parameters[PenaltyFactorParameterName];

    public bool OptimizeParameters {
      get => OptimizerParametersParameter.Value.Value;
      set => OptimizerParametersParameter.Value.Value = value;
    }

    public int ConstantOptimizationIterations {
      get => ConstantOptimizationIterationsParameter.Value.Value;
      set => ConstantOptimizationIterationsParameter.Value.Value = value;
    }

    public bool UseSoftConstraints {
      get => UseSoftConstraintsParameter.Value.Value;
      set => UseSoftConstraintsParameter.Value.Value = value;
    }

    public IBoundsEstimator BoundsEstimator {
      get => BoundsEstimatorParameter.Value;
      set => BoundsEstimatorParameter.Value = value;
    }

    public double PenalityFactor {
      get => PenaltyFactorParameter.Value.Value;
      set => PenaltyFactorParameter.Value.Value = value;
    }


    public override bool Maximization => false; // NMSE is minimized

    #endregion

    #region Constructors/Cloning

    [StorableConstructor]
    protected NMSESingleObjectiveConstraintsEvaluator(StorableConstructorFlag _) : base(_) { }

    protected NMSESingleObjectiveConstraintsEvaluator(
      NMSESingleObjectiveConstraintsEvaluator original, Cloner cloner) : base(original, cloner) { }

    public NMSESingleObjectiveConstraintsEvaluator() {
      Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersParameterName,
        "Define whether optimization of numeric parameters is active or not (default: false).", new BoolValue(false)));
      Parameters.Add(new FixedValueParameter<IntValue>(ParameterOptimizationIterationsParameterName,
        "Define how many parameter optimization steps should be performed (default: 10).", new IntValue(10)));
      Parameters.Add(new FixedValueParameter<BoolValue>(UseSoftConstraintsParameterName,
        "Define whether the constraints are penalized by soft or hard constraints (default: false).", new BoolValue(false)));
      Parameters.Add(new ValueParameter<IBoundsEstimator>(BoundsEstimatorParameterName,
        "The estimator which is used to estimate output ranges of models (default: interval arithmetic).", new IntervalArithBoundsEstimator()));
      Parameters.Add(new FixedValueParameter<DoubleValue>(PenaltyFactorParameterName,
        "Punishment factor for constraint violations for soft constraint handling (fitness = NMSE + penaltyFactor * avg(violations)) (default: 1.0)", new DoubleValue(1.0)));
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new NMSESingleObjectiveConstraintsEvaluator(this, cloner);
    }

    #endregion

    public override IOperation InstrumentedApply() {
      var rows = GenerateRowsToEvaluate();
      var tree = SymbolicExpressionTreeParameter.ActualValue;
      var problemData = ProblemDataParameter.ActualValue;
      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
      var estimationLimits = EstimationLimitsParameter.ActualValue;
      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;

      if (OptimizeParameters) {
        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows,
          false, ConstantOptimizationIterations, true,
          estimationLimits.Lower, estimationLimits.Upper);
      } else {
        if (applyLinearScaling) {
          var rootNode = new ProgramRootSymbol().CreateTreeNode();
          var startNode = new StartSymbol().CreateTreeNode();
          var offset = tree.Root.GetSubtree(0) //Start
                                .GetSubtree(0); //Offset
          var scaling = offset.GetSubtree(0);

          //Check if tree contains offset and scaling nodes
          if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
            throw new ArgumentException($"{ItemName} can only be used with LinearScalingGrammar.");

          var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
          rootNode.AddSubtree(startNode);
          startNode.AddSubtree(t);
          var newTree = new SymbolicExpressionTree(rootNode);

          //calculate alpha and beta for scaling
          var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);

          var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
          OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
            out var errorState);

          if (errorState == OnlineCalculatorError.None) {
            //Set alpha and beta to the scaling nodes from ia grammar
            var offsetParameter = offset.GetSubtree(1) as ConstantTreeNode;
            offsetParameter.Value = alpha;
            var scalingParameter = scaling.GetSubtree(1) as ConstantTreeNode;
            scalingParameter.Value = beta;
          }
        } // else: alpha and beta are evolved
      }

      var quality = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows,
        BoundsEstimator, UseSoftConstraints, PenalityFactor);
      QualityParameter.ActualValue = new DoubleValue(quality);

      return base.InstrumentedApply();
    }

    public static double Calculate(
      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
      ISymbolicExpressionTree tree,
      double lowerEstimationLimit, double upperEstimationLimit,
      IRegressionProblemData problemData, IEnumerable<int> rows,
      IBoundsEstimator estimator,
      bool useSoftConstraints = false, double penaltyFactor = 1.0) {

      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      var constraints = Enumerable.Empty<ShapeConstraint>();
      if (problemData is ShapeConstrainedRegressionProblemData scProbData) {
        constraints = scProbData.ShapeConstraints.EnabledConstraints;
      } 
      var intervalCollection = problemData.VariableRanges;

      var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
      var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues,
        out var errorState);

      if (errorState != OnlineCalculatorError.None) {
        return 1.0;
      }

      var constraintViolations = IntervalUtil.GetConstraintViolations(constraints, estimator, intervalCollection, tree);

      if (constraintViolations.Any(x => double.IsNaN(x) || double.IsInfinity(x))) {
        return 1.0;
      }

      if (useSoftConstraints) {
        if (penaltyFactor < 0.0)
          throw new ArgumentException("The parameter has to be >= 0.0.", nameof(penaltyFactor));

        var weightedViolationsAvg = constraints
          .Zip(constraintViolations, (c, v) => c.Weight * v)
          .Average();

        return Math.Min(nmse, 1.0) + penaltyFactor * weightedViolationsAvg;
      } else if (constraintViolations.Any(x => x > 0.0)) {
        return 1.0;
      }

      return nmse;
    }

    public override double Evaluate(
      IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData,
      IEnumerable<int> rows) {
      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
      EstimationLimitsParameter.ExecutionContext = context;
      ApplyLinearScalingParameter.ExecutionContext = context;

      var nmse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
        problemData, rows, BoundsEstimator, UseSoftConstraints, PenalityFactor);

      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
      EstimationLimitsParameter.ExecutionContext = null;
      ApplyLinearScalingParameter.ExecutionContext = null;

      return nmse;
    }

    public override double Evaluate(IRegressionProblemData problemData,
      ISymbolicExpressionTree solution,
      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
      IEnumerable<int> rows = null,
      bool applyLinearScaling = true,
      double lowerEstimationLimit = double.MinValue,
      double upperEstimationLimit = double.MaxValue) {

      if (OptimizeParameters) {
        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(
          interpreter, 
          solution, 
          problemData, 
          rows,
          false, 
          ConstantOptimizationIterations, 
          true,
          lowerEstimationLimit, 
          upperEstimationLimit);
      } else {
        if (applyLinearScaling) {
          var rootNode = new ProgramRootSymbol().CreateTreeNode();
          var startNode = new StartSymbol().CreateTreeNode();
          var offset = solution.Root.GetSubtree(0) //Start
                                    .GetSubtree(0); //Offset
          var scaling = offset.GetSubtree(0);

          //Check if tree contains offset and scaling nodes
          if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
            throw new ArgumentException($"{ItemName} can only be used with LinearScalingGrammar.");

          var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
          rootNode.AddSubtree(startNode);
          startNode.AddSubtree(t);
          var newTree = new SymbolicExpressionTree(rootNode);

          //calculate alpha and beta for scaling
          var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);

          var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
          OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
            out var errorState);

          if (errorState == OnlineCalculatorError.None) {
            //Set alpha and beta to the scaling nodes from ia grammar
            var offsetParameter = offset.GetSubtree(1) as ConstantTreeNode;
            offsetParameter.Value = alpha;
            var scalingParameter = scaling.GetSubtree(1) as ConstantTreeNode;
            scalingParameter.Value = beta;
          }
        } // else: alpha and beta are evolved
      }
      return Calculate(
        interpreter, 
        solution, 
        lowerEstimationLimit, 
        upperEstimationLimit, 
        problemData, 
        rows ?? problemData.TrainingIndices,
        BoundsEstimator,
        UseSoftConstraints,
        PenalityFactor);
    }
  }
}